In so-called dual-spectrum projection radiography or dual-energy projection imaging, often abbreviated to DE imaging, an examination subject, e.g. a patient, is captured using two different x-ray spectra in order thereby to generate two projection images (raw images) of the region of interest.
By suitably combining the two raw images, two radiologically different materials such as soft tissue and bone can thus be differentiated.
Within the framework of a widely used more qualitative method of dual-energy projection imaging, only grayscale images are produced, while in the case of quantitative dual-energy imaging, physical quantities are reconstructed, i.e. reconstruction values such as material thicknesses (in cm) or areal mass densities (in g/cm2), hereinafter also referred to as mass densities for short, are determined from the raw image data.
A known disadvantage of DE imaging is the marked increase in image noise compared to the raw images. For an exact quantitative reconstruction in terms of determining reconstruction values, a mathematically generally ill-conditioned system of nonlinear equations must be solved. However, this is associated with an increase in image noise.
Therefore, various noise filtering methods are already being used at present. However, these are either not based on the physically correct nonlinear model or merely constitute image post-processing in which negative correlations between separated material images are skillfully utilized for the purpose of noise reduction. Such methods are known e.g. from the articles “Quantitative evaluation of noise reduction strategies in dual-energy imaging” by R. J. Warp and J. T. Dobbins from Med. Phys. 30 (2), February 2003, and “An Algorithm for noise suppression in Dual Energy CT Material Density Images” by W. A. Kalender, E. Klotz and L. Kostaridou from IEEE Trans. Med Imaging, Vol. 7, No. 3, September 1988, 218-224, and also “A correlated noise reduction algorithm for dual-energy digital subtraction angiography” by C. H. McCollough, M. S. VanLysel, W. W. Peppler and C. A. Mistretta from Med. Phys. 16 (6), November/December 1989, 873-880.
These noise filterings and/or methods for reducing image noise are therefore non-optimal in terms of basic approach.